MSGSU ISTATISTIK BOLUMU - IST 335/s R ILE ISTATISTIKSEL PROGRAMLAMA DERS NOTLARI 2021-22 by ozge.ozdamar@msgsu.edu.tr is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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REFERENCES

Links

GITHUB

Cheat Sheets

R Studio IDE

LIBRARIES

.packages = c("RXKCD","beepr","fortunes","Rcmdr", "fun", "plotly", "magrittr", "networkD3", "dygraphs","devtools")
.inst <- .packages %in% installed.packages()
if(length(.packages[!.inst]) > 0) install.packages(.packages[!.inst])
lapply(.packages, require, character.only=TRUE)

1 1. Introduction

1.1 what is R?

R is a language and environment for statistical computing and graphics, similar to the S language originally developed at Bell Labs

1.2 + vs -

open source
R has state-of-the-art graphics capabilities. If you want to visualize complex data, R has the most comprehensive and powerful feature set available
R can have a steep learning curve.
Three constraints on computing performance CPU, RAM, and disk I/O
R is interpreted on the fly
R is single-threaded
R requires all data to be loaded into memory
Algorithm design affects time and space complexity

1.3 R can..

library(plotly)
library(magrittr)

mtcars$am[which(mtcars$am == 0)] <- 'Automatic'
mtcars$am[which(mtcars$am == 1)] <- 'Manual'
mtcars$am <- as.factor(mtcars$am)

p <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec, color = ~am, colors = c('#BF382A', '#0C4B8E')) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Weight'),
                     yaxis = list(title = 'Gross horsepower'),
                     zaxis = list(title = '1/4 mile time')))
p

library(networkD3)
data(MisLinks, MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             Group = "group", opacity = 0.4)

library(dygraphs)
 dygraph(nhtemp, main = "New Haven Temperatures") %>%
  dyRangeSelector(dateWindow = c("1920-01-01", "1960-01-01"))

1.4 download R

CRAN- The Comprehensive R Archive Network: https://cran.r-project.org/

1.5 R console

1.6 base installiation

Base installation: base, datasets, utils, grDevices, graphics, stats, methods … Packages

Base package contains the basic functions which let R function as a language: arithmetic, input/output, basic programming support, etc.

Packages are collections of R functions, data, and compiled code in a well-defined format.

The directory where packages are stored on your computer is called the library.

library()

1.7 Task views:

https://cran.r-project.org/ > Task Views

1.8 cran packages

https://cran.r-project.org/ > Packages

R Home Page : https://www.r-project.org/ > Bioconductor

1.9 package web page

1.10 package information

Version :

Depends :

Imports : packages listed here must be present for the package to work

Suggests : package can use these packages, but doesn’t require them

Citation :

Reference manual:

Vingette :

Source :

1.11 inside packages

1.11.1 Data

data()  # list of data of installed packages
data(mtcars) # load data mtcars from package datasets
mtcars # print data to screen

###Functions

sum(mtcars$mpg)

## [1] 642.9

?sum #help for function
??sum # fuzzy search for help

###examples

example(mtcars)

## 
## mtcars> require(graphics)
## 
## mtcars> pairs(mtcars, main = "mtcars data", gap = 1/4)

## 
## mtcars> coplot(mpg ~ disp | as.factor(cyl), data = mtcars,
## mtcars+        panel = panel.smooth, rows = 1)

## 
## mtcars> ## possibly more meaningful, e.g., for summary() or bivariate plots:
## mtcars> mtcars2 <- within(mtcars, {
## mtcars+    vs <- factor(vs, labels = c("V", "S"))
## mtcars+    am <- factor(am, labels = c("automatic", "manual"))
## mtcars+    cyl  <- ordered(cyl)
## mtcars+    gear <- ordered(gear)
## mtcars+    carb <- ordered(carb)
## mtcars+ })
## 
## mtcars> summary(mtcars2)
##       mpg        cyl         disp             hp             drat      
##  Min.   :10.40   4:11   Min.   : 71.1   Min.   : 52.0   Min.   :2.760  
##  1st Qu.:15.43   6: 7   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080  
##  Median :19.20   8:14   Median :196.3   Median :123.0   Median :3.695  
##  Mean   :20.09          Mean   :230.7   Mean   :146.7   Mean   :3.597  
##  3rd Qu.:22.80          3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920  
##  Max.   :33.90          Max.   :472.0   Max.   :335.0   Max.   :4.930  
##        wt             qsec       vs             am     gear   carb  
##  Min.   :1.513   Min.   :14.50   V:18   automatic:19   3:15   1: 7  
##  1st Qu.:2.581   1st Qu.:16.89   S:14   manual   :13   4:12   2:10  
##  Median :3.325   Median :17.71                         5: 5   3: 3  
##  Mean   :3.217   Mean   :17.85                                4:10  
##  3rd Qu.:3.610   3rd Qu.:18.90                                6: 1  
##  Max.   :5.424   Max.   :22.90                                8: 1

###demonstrations

demo() # list of demos of installed packages
demo(graphics)

###vignettes

vignette() # list of vignettes of installed packages

###GUI packages

library(Rcmdr) # Statistics  GUI

###fun :)

library(RXKCD)
searchXKCD("programing ")
getXKCD(353)

library(beepr)
beep(4)

library(fortunes)
fortune()

library(fun)
gomoku()

library(fun)
if (interactive()) {
    if (.Platform$OS.type == "windows") 
        x11() else x11(type = "Xlib")
    sliding_puzzle()
    sliding_puzzle(z = matrix(0:11, 3, 4))
}

devtools::install_github('RLesur/Rcade')
Rcade::games
Rcade::games$Pacman

Rcade::games$`2048`

2 other package sources:

help	Link
Microsoft R Open:latest packages	https://mran.microsoft.com/spotlight/
Rdrr.io: Cran + bioconductor + Github + R-Forge	https://rdrr.io/all/
rOpenSci packages	https://ropensci.org/packages/
RDocumentation: CRAN + BioConductor + Github	https://www.rdocumentation.org/
AWESOME R	https://awesome-r.com/
github	https://github.com/qinwf/awesome-R
METACRAN	https://www.r-pkg.org/
CRANBERRIES	http://dirk.eddelbuettel.com/cranberries/
BIOCONDUCTOR	https://www.bioconductor.org/packages/release/BiocViews.html
RSTUDIO	https://rviews.rstudio.com/

3 for help

help	Link
email lists	https://www.r-project.org/mail.html
stackoverflow	https://stackoverflow.com/
R search engine	http://search.r-project.org/
R seek engine	https://rseek.org/

4 IDE / GUIS

IDE / GUI	Link
RSTUDIO	https://www.rstudio.com A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.
Revolution R Enterprise	https://mran.microsoft.com Revolution R would be offered free to academic users and commercial software would focus on big data, large scale multiprocessor functionality.
EMACS + ESS	http://ess.r-project.org Emacs Speaks Statistics is an add-on package for emacs text editors.
IRkernel	https://github.com/IRkernel/IRkernel R kernel for Jupyter.
StatET	http://www.walware.de/goto/statet An Eclipse based IDE for R
NOTEPAD ++	https://notepad-plus-plus.org/download/v7.6.3.html
SCIVIEWS	http://www.sciviews.org
R-BRAIN	https://r-brain.io/en/
RKWARD	https://rkward.kde.org
Radiant	https://radiant-rstats.github.io/docs/ A platform-independent browser-based interface for business analytics in R, based on the Shiny
RTVS R TOOLS FOR VISUAL STUDIO	https://docs.microsoft.com/en-us/visualstudio/rtvs/?view=vs-2017
TINN-R	https://sourceforge.net/projects/tinn-r/
JGR	https://www.rforge.net/JGR/
R AnalyticFlow	https://r.analyticflow.com/en/
JASP	https://jasp-stats.org A complete package for both Bayesian and Frequentist methods, that is familiar to users of SPSS.
Rattle	https://rattle.togaware.com
Vim-R	https://github.com/vim-scripts/Vim-R-plugin Vim plugin for R
Nvim-R	https://github.com/jalvesaq/Nvim-R Neovim plugin for R
Bio7	https://bio7.org A IDE contains tools for model creation, scientific image analysis and statistical analysis for ecological modelling.
R Commender	https://socialsciences.mcmaster.ca/jfox/Misc/Rcmdr/ A package that provides a basic graphical user interface.
Deducer	http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual?from=Main.HomePage A Menu driven data analysis GUI with a spreadsheet like data editor.

5 RStudio

5.1 RStudio IDE

Tools -> Global Options -> Pane Layout

5.1.1 Source Editor Pane

5.1.2 Environment and History Pane

5.1.3 Console Pane

Command Completion: Tab Command History Popup: Ctrl + arrow up Clear Console : Ctrl + L Go through historical command : arrow up

5.1.3.1

6 Functions for library management

R has a workspace known as the global environment that can be used to store the results of calculations, and many other types of objects.

get working directory

getwd()

set working directory

setwd(...)

dir()

Save history

savehistory(file="isim.Rhistory")
loadhistory(file="isim.Rhistory")

quit R

q()

your library’s path

# your library's path 
.libPaths()

list of packages in your library

library()

details of all packages installed in the specified libraries

installed.packages()

list of functions in a package

library(help = "base")

install packages

install.packages("PackageName")     # from CRAN
biocLite(PackageName)       # from Bioconductor
Devtolls::install_github(PackageName) # fom Github

load packages

library(PackageName)
require(PackageName)

list of loaded packages

search()

directories of attached (loaded) packages

searchpaths()

update library

update.packages()

7 R as a calculator

## [1] 5

5+7

## [1] 12

10.5

## [1] 10.5

56/2

## [1] 28

3*6

## [1] 18

5-10

## [1] -5

3^2

## [1] 9

14/5+3

## [1] 5.8

14/(5+3)

## [1] 1.75

8 Named storage

8.1 variable names

In R, an object is anything that can be assigned to a variable. This includes constants, data structures, functions, and even graphs. Objects have a mode (which describes how the object is stored) and a class (which tells generic functions like print how to handle it).

atama operatoru ters ok isaretidir <-

a <- 5 # my first variable :P 
a

## [1] 5

b <- 6*8-4
b

## [1] 44

b <- b*10

8.2 nesne isimleri:

case sensitive : A ile a farkli

Turkce karakter kullanilmamali

Rakam ile baslamamali, alfanumerik olabilir : b1, m5

imlecler: _ , .

ingilizce komut olabilecek kelimeleri nesne ismi olarak kullanilmamali (print, scan, c)

degisken isimleri icin mantik gelistiriniz:

Var_name2 : Valid - contains underscores, dot, numbers, or letters.

2var_name : Invalid - Starts with a number

Var_name% : Invalid - Contains the percentage (%) sign.

.2var_name : Invalid - Starts with a dot followed by a number.

.var_name, var.name : Valid - Starts with a dot. Dot should never be followed by a number.

_var_name : Invalid - Starts with an underscore.

9 objects

ob1 <- 54 
ob2 <- "merhaba"
ob3 <- 'F'
ob4 <- TRUE
ob5 <- F
ob6 <-c(1,4,5) # this is a vector
ob7 <- mtcars
ob8 <- 54L

10 function for objects

ob1 # print object to screen

## [1] 54

print(ob1)

## [1] 54

class(ob1)

## [1] "numeric"

mode(ob1)

## [1] "numeric"

typeof(ob1)

## [1] "double"

class(ob8)

## [1] "integer"

mode(ob8)

## [1] "numeric"

typeof(ob8)

## [1] "integer"

str(ob2) #structure of object

##  chr "merhaba"

dim(ob2) # dimension of object

## NULL

length(ob2) # length of object

## [1] 1

names(ob2) #names of objects in avaible

## NULL

summary(ob2) #

##    Length     Class      Mode 
##         1 character character

is.vector(ob1) # ask object type

## [1] TRUE

as.matrix(ob1) # change object type

##      [,1]
## [1,]   54

rm(ob1) #remove object
objects()

##  [1] "a"        "b"        "MisLinks" "MisNodes" "mtcars"   "mtcars2" 
##  [7] "ob2"      "ob3"      "ob4"      "ob5"      "ob6"      "ob7"     
## [13] "ob8"      "p"

ls()

##  [1] "a"        "b"        "MisLinks" "MisNodes" "mtcars"   "mtcars2" 
##  [7] "ob2"      "ob3"      "ob4"      "ob5"      "ob6"      "ob7"     
## [13] "ob8"      "p"

#remove()
#rm()

Object	class()	mode()	typeof()
ob1<-54	numeric	numeric	double
ob2<-“merhaba”	character	character	character
ob3<-‘F’	character	character	character
ob4<-TRUE	logical	logical	logical
ob5<-F	logical	logical	logical
ob6<-c(1,4,5)	numeric	numeric	double
ob7<-mtcars	data.frame	list	list
ob8<-54L	data.frame	list	list

11 Operators

11.1 1. Assignment Operators

<-
=
<<-
->
->>

v1 <- c(3,1,TRUE,2+3i)
v2 <<- c(3,1,TRUE,2+3i)
v3 = c(3,1,TRUE,2+3i)
v3

## [1] 3+0i 1+0i 1+0i 2+3i

15-> v4
v4

## [1] 15

http://stat.ethz.ch/R-manual/R-patched/library/base/html/assignOps.html

11.2 2.Logical Operators

These are applicable to vectors that are known to be complex, numeric, or, logical. The value TRUE will be given to anything that is larger than 1. You will then compare the first element of the vector with the second vector’s corresponding element.

Table	Second Header
AND	“&”
OR	”
NOT	“!”
Logical AND	“&&”
Logical OR	”

Operators & and | perform element-wise operation producing result having length of the longer operand. But && and || examines only the first element of the operands resulting into a single length logical vector.

Zero is considered FALSE and non-zero numbers are taken as TRUE.

11.2.1 `&` : AND

It is called Element-wise Logical AND operator. It combines each element of the first vector with the corresponding element of the second vector and gives a output TRUE if both the elements are TRUE.

x1<-c(1,2,3,4)
x1

## [1] 1 2 3 4

x2<-c(10,20,30,40)
x2

## [1] 10 20 30 40

x1&x2

## [1] TRUE TRUE TRUE TRUE

v <- c(3, 1, TRUE, 2+3i) 
v

## [1] 3+0i 1+0i 1+0i 2+3i

t <- c(4, 1, FALSE, 2+3i)
t

## [1] 4+0i 1+0i 0+0i 2+3i

v&t

## [1]  TRUE  TRUE FALSE  TRUE

11.2.2 `|` : OR

It is called Element-wise Logical OR operator. It combines each element of the first vector with the corresponding element of the second vector and gives a output TRUE if one the elements is TRUE.

x1|x2

## [1] TRUE TRUE TRUE TRUE

v|t

## [1] TRUE TRUE TRUE TRUE

11.2.3 `!` : NOT

It is called Logical NOT operator. Takes each element of the vector and gives the opposite logical value.

!x1

## [1] FALSE FALSE FALSE FALSE

!x2

## [1] FALSE FALSE FALSE FALSE

!v

## [1] FALSE FALSE FALSE FALSE

!t

## [1] FALSE FALSE  TRUE FALSE

11.2.4 `&&` : Logical AND

Called Logical AND operator. Takes first element of both the vectors and gives the TRUE only if both are TRUE.

x1&&x2

## [1] TRUE

v&&t

## [1] TRUE

11.2.5 `||` : Logical OR

Called Logical OR operator. Takes first element of both the vectors and gives the TRUE if one of them is TRUE.

x1||x2

## [1] TRUE

v||t

## [1] TRUE

11.3 3. Relational Operators

Relational Operators are known to make comparisons between the first and second elements. With that, you’’ll get Boolean values as results.

Operator	Meaning
1. <	Less than
2. >	Greater than
3. <=	Less than or equal to
4. >=	Greater than or equal to
5. ==	Equal to
6. !=	Not equal to

v <- c(2, 5.5, 6, 9) 
t <- c(8, 2.5, 14, 9)
v < t

## [1]  TRUE FALSE  TRUE FALSE

v > t

## [1] FALSE  TRUE FALSE FALSE

v <= t

## [1]  TRUE FALSE  TRUE  TRUE

v >= t

## [1] FALSE  TRUE FALSE  TRUE

v == t

## [1] FALSE FALSE FALSE  TRUE

v != t

## [1]  TRUE  TRUE  TRUE FALSE

11.4 4. Arithmetic Operators

Arithmetic Operators are used to carry out mathematical operations

Operator	Meaning
1. +	Addition
2. -	Subtraction
3. *	Multiplication
4. /	Division
5. ^	Exponent
6. %%	Modulus (Remainder from division)
7. %/%	Integer Division

v <- c(2, 5.5, 6)
t <- c(8, 3, 4)
v + t

## [1] 10.0  8.5 10.0

v - t

## [1] -6.0  2.5  2.0

v * t

## [1] 16.0 16.5 24.0

v / t

## [1] 0.250000 1.833333 1.500000

v ^ t

## [1]  256.000  166.375 1296.000

v %% t

## [1] 2.0 2.5 2.0

v %/% t

## [1] 0 1 1

11.5 5. Miscellaneous Operators

These operators are used to for specific purpose and not general mathematical or logical computation.

operator	meaning
:	Colon operator. It creates the series of numbers in sequence for a vector.
%in%	This operator is used to identify if an element belongs to a vector.
%*%	This operator is used to multiply matrices

v1 <- 8
v2 <- 12
t <- 1:10

v1 %in% t

## [1] TRUE

v2 %in% t

## [1] FALSE

M = matrix(1:6, nrow = 2,ncol = 3,byrow = TRUE)
t = M %*% t(M)
t

##      [,1] [,2]
## [1,]   14   32
## [2,]   32   77

12 Workspace functions

getwd()
setwd()
ls()
rm()
help(options)
options()
history()
savehistory()
load(history)
save.image()
save(...)
load()
q

13 HELP

help.start()
help(sum) # or
?sum
help.search(sum) # or
??sum
example(sum)
RSiteSearch("sum")
apropos("sum", mode="function")
data()
demo()
vignette()
vignette("sum")

14 Special Numbers

NA : Not Available

NaN : Not a Number

Inf : Infinity

-Inf : Negative Infinity

14.1 `NA`

In R, the NA values (Not available) are used to represent missing values.

x1<-NA
x1

## [1] NA

is.na(x1) # returns TRUE of x is missing

## [1] TRUE

is.nan(x1)

## [1] FALSE

is.finite(x1)

## [1] FALSE

is.infinite(x1)

## [1] FALSE

class(x1)

## [1] "logical"

14.2 `NAN`

Sometimes, a computation will produce a result that makes little sense. In these cases, R will often return NaN (meaning “not a number”)

x2<-0/0
x2

## [1] NaN

is.na(x2)

## [1] TRUE

is.nan(x2)

## [1] TRUE

is.finite(x2)

## [1] FALSE

is.infinite(x2)

## [1] FALSE

class(x2)

## [1] "numeric"

Inf - Inf

## [1] NaN

14.3 `Inf`

If a computation results in a number that is too big, R will return Inf for a positive number and -Inf for a negative number.This is also the value returned when you divide by 0:

x3a<-10/0
x3a

## [1] Inf

is.na(x3a)

## [1] FALSE

is.nan(x3a)

## [1] FALSE

is.finite(x3a)

## [1] FALSE

is.infinite(x3a)

## [1] TRUE

class(x3a)

## [1] "numeric"

 2 ^ 1024

## [1] Inf

14.4 `-Inf

x3b <- -10/0
x3b

## [1] -Inf

is.na(x3b)

## [1] FALSE

is.nan(x3b)

## [1] FALSE

is.finite(x3b)

## [1] FALSE

is.infinite(x3b)

## [1] TRUE

class(x3b)

## [1] "numeric"

 -2 ^ 1024

## [1] -Inf

14.5 NULL

NULL is often used to explicitly define an “empty” entity, which is quite different from a “missing” entity specified with NA.

NULL is often used as an argument in functions to mean that no value was assigned to the argument. Additionally, some functions may return NULL.

x1<-NULL
x1

## NULL

x2<-NA # NA is with index number
x2

## [1] NA

x1<-c(2,5,NA,6)
x1

## [1]  2  5 NA  6

length(x1)

## [1] 4

x2<-c(2,5,NULL,6)
x2

## [1] 2 5 6

length(x2)

## [1] 3

NULL+5

## numeric(0)

NA +5

## [1] NA

5<=NULL

## logical(0)

R Introduction

ozge.ozdamar@msgsu.edu.tr

October 28 2021